Eight years of community structure monitoring through recreational citizen science at the “SS Thistlegorm” wreck (Red Sea)

Large artificial coral reef communities, such as those thriving on sunken shipwrecks, tend to mirror those of nearby natural coral reefs and their long-term dynamics may help future reef resilience to environmental change. We examined the community structure of the world-renown “SS Thistlegorm” wreck in the northern Red Sea from 2007 through 2014, analyzing data collected during the recreational citizen science Red Sea monitoring project “Scuba Tourism for the Environment”. Volunteer divers collected data on 6 different diving parameters which included the date of the dive, maximum depth, average depth, temperature, dive time, hour of dive, and gave an abundance estimation of sighted taxa from a list of 72 target taxa. Although yearly variations in community structure were significant, there was no clear temporal trend, and 71 of all 72 target taxa were sighted throughout the 8 years. The 5 main taxa driving variations among year clusters in taxa presence/absence (Soft Tree Coral—Dendronephthya spp., Giant Moray—Gymnothorax javanicus, Squirrel Fish—Sargocentron spp., Humpback Batfish—Platax spp., and Caranxes—Carangidae) and taxa abundance (Soft Tree Coral, Giant Moray, Red Sea Clownfish—Amphiprion bicinctus, Napoleon Wrasse—Cheilinus undulatus, and Caranxes) data were determined. The “SS Thistlegorm” provides a compelling example of how artificial coral reefs can sustain a well-established community structure similar to those of their natural counterparts.

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Introduction
The biological communities of tropical and sub-tropical coral reefs support some of the highest biodiversity in the world [1] and provide a wide array of socio-economic services including coastal protection, water quality and chemical cycling, fisheries and materials markets, and experiential benefits [2]. Despite their assortment of values, coral species homogenization [3], species extinctions [4], and overall changes of coral communities are expected in future [5]. In fact, the Intergovernmental Panel on Climate Change projections [6] and current research indicate a multitude of threats to coral reefs including but not limited to ocean warming and acidification, overfishing, and rise in coastal human populations and impacts [7].
One of the largest coral reef systems of the world, with notably high endemism, lies within the Red Sea [8]. Due to its peculiar geo-evolutionary past resulting in large latitudinal gradients of sea surface temperature and salinity [8] the Red Sea provides an attractive location for studying the response of coral reefs to threats of their projected immediate future. The northern Red Sea is considered a coral refuge because, although it has experienced some of the highest sea surface temperature anomalies, there have been very few coral bleaching events compared to its southern portion [7,9]. Notwithstanding the relative resilience of reefs in the northern Red Sea, there is still evidence of general coral colony size decline and species homogenization throughout [10], having the potential to induce changes in the biological communities they support. The importance of monitoring community variation lies not only in its fundamental value but also the economic income from fisheries [11] and especially the tourist industry, providing an array of socio-economic opportunities for many coastal countries [12]. Unfortunately, there is general lack of information regarding Red Sea coral communities, which are relatively understudied when compared to the Great Barrier Reef or reef systems of the Caribbean Sea [13]. This can pose conservation challenges when attempting to monitor and analyze crucial changes in the biological communities of the Red Sea.
An innovative and upcoming approach to gathering large amounts of data in a time efficient way is the use of citizen science. This approach is not so novel in terms of human history, but current technological advances of citizen science in the spreading and sharing of information and data [14,15] have benefits and uses that are now an integral part of modern science [16]. Citizen science can be a critical asset in helping researchers, policy makers, and stakeholders overcome resource limitations (e.g., economical, temporal, geographical). Community-based management, and community-based monitoring are two of the main methodologies of the citizen science approach. The former involves direct participation of citizens and stakeholders in management decisions [17], while the latter implies the collaboration of citizens and stakeholder for data collection to monitor, track, and respond to areas of concern (e.g., environmental health) [18]. Scuba Tourism for the Environment (STE; www.steproject.org) has been a great example of utilizing large numbers of volunteers for community-based monitoring throughout much of the Red Sea [19]. Project volunteers participating in the STE program collected ecological community data with the assistance of active dive centers of their choosing and trained guides throughout Egypt. Recreational citizen science was applied throughout the project and therefore did not require a behavioral change of the divers. Reliability trials of the STE project, based on correlations to reference researchers, indicate that Consistency ranked the lowest parameter while Percent Identified scored the highest [20], showing that while divers can accurately identify most taxa there is a tendency to focus on certain organisms due to personal interest. Overall, with correct development to the needs of specific projects, data collected through citizen science can reliably support monitoring efforts [20,17,16]. The SS Thistlegorm, a world class dive site [21] and one of topmost visited wreck dives in the world [22], was one of the dive locations monitored in the STE project and is the focus of this study.
Built in 1940, the SS Thistlegorm was quickly taken over for the war efforts of the British military and after 3 successful voyages, her fourth and final incomplete journey from Glasgow (UK) to Alexandria (Egypt) resulted in her ultimate resting ground in the Straights of Gubal on the 6 th of October 1941, 30 meters below the sea surface [23] Following two hits by German Heinkel HE 111 bombers to the fourth hold and the engine room (where the ammunition was stored), the 128 m long cargo steam ship was split in two by munitions explosions and sunk within minutes. In the 80 plus years since the subsequent discovery of the wreck by Jacques-Yves Cousteau in 1955, and the development of the Sharm el Sheikh resort in the 1990s, the SS Thistlegorm has recruited up to 175,000 visiting divers per year from around the world [23]. Due to its high popularity and frequent visitors, there are rising concerns regarding the structural longevity of this historic site. Irresponsibly moored boats, inexperienced divers, and even air bubbles can cause irreparable damage to the structural integrity of underwater relics [24] as well as the biological community residing there [25].
A dedicated team of divers, archeologists, and researchers developed the Thistlegorm Project (www.thethistlegormproject.com) to survey the site using 360 videography for the purposes of creating an accurate archeological 3D survey and to raise awareness for the protection of underwater cultural heritage sites.
Aside from the historical importance of the SS Thistlegorm wreck, the ship has recruited numerous marine life forms through the decades spent on the sandy bottom close to a natural coral reef and far from touristic resorts. In fact, this underwater, man-made structure can be regarded as a an artificial reef community, though "accidental" in nature. Since the 1960's, artificial reefs have been used for a variety of purposes including biodiversity conservation [26], fisheries management [27], and tourist locations [28]. One study in Eilat (Red Sea) designed and submerged multiple concrete structures to test whether artificial reefs could decrease diving pressures on the surrounding natural reef areas, concluding that small scale structures, knowledge of such structures within the local diving community, and education of divers could be sufficient [29]. The provision of migratory networks for coral movement and resettlement within changing oceans [30] is another application for artificial reefs, though highly debated and rather unexplored. Organismal community recruitment and interchange is an evident and well observed occurrence when any new colonizable object, be it an island or a plastic bottle, appears near a source population. In accordance with island biogeography theory [31], the evolution of this community is much dependent on the size of the object in question, the time it is in existence, and its distance from the source. Sunken warships have been shown to serve as exceptionally good artificial substrates for coral reefs as their size and complexity offer a multitude of opportunities for microhabitats, and in cases where they extend down to 30-40 m, the cooler waters can provide respite to corals from warming oceans but still sufficient light for their symbiotic zooxanthellae [32]. Many shipwrecks in the North Sea have supported hard substrate communities throughout the Belgian Continental Shelf which has otherwise been transformed into a soft-bottom environment due to anthropogenic activity [33,34] Thanks to the steel structure of the SS Thistlegorm, recruitment of various tolerant sessile species typical of hard substrates was enabled in the sandy bottom environment within the Straights of Gubal and a well-established coral community can now be witnessed, but no information is available in the scientific literature.
The aim of this study was to characterize the community structure of one of the most well-known and top-raking dive sites in the world, the SS Thistlegorm, over 8 years of monitoring through recreational citizen science.

Data Collection and Isolation
Data were collected within the Scuba Tourism for the Environment -Red Sea Biodiversity Monitoring Program (STE; www.steproject.org), whose efforts include the collection of large scale spatial-temporal biodiversity data within the Red Sea from 2007 to 2015 using a recreational citizen science approach where the behavior of the underwater tourists is unaltered during surveys [19].

Preliminary Treatment and Analyses
Data were split into three data sets: 1) taxa abundance; 2) taxa presence/absence (p/a), obtained through an overall transformation of taxa abundance; and 3) diving parameters. The sighting frequency (SF%) and Relative Abundance (RA) for each taxon were calculated as such [19]: For a general picture of the community composition and to identify outliers within each year, 2dimensional plots were created using non-metric multidimensional scaling (MDS) from the Bray-Curtis resemblance matrices of both taxa data sets [35,36]. A BVSTEP "Best" test was performed on both taxa data sets to find the subset of taxa that best represented the community of each year (BVSTEP [37]). These subsets of taxa were later used for aggregation of the data in the temporal analysis A distance matrix of normalized diving parameters was created using Euclidean distances.
Both taxa distance matrices were tested for a relationship with the distance matrices of the diving parameters using the Relate test to determine whether the variation between years was affected only by the change in time or also by diving parameters (Relate, [38]). Additional distance based linear models (DistLM, [39]) were ran for each year with a significant relationship between the taxa data and diving parameters, providing the individual parameters that significantly affected the variation of community structure in each year.
To test the differences in diving parameters over the years, a permutational multivariate analysis of variance (PERMANOVA, [39]) was designed with the fixed factor of "year" and run on the Euclidean resemblance matrix of the normalized aggregate diving parameters. Both main and pairwise tests were run. Additional PERMANOVAs were created for individual diving parameters to test whether each different diving parameter was significantly different among the years.
All the above analyses were done using PRIMER-E version 6 with PERMANOVA+ version 1 software (PRIMER-E, Ltd., Ivybridge, UK).
Seven of the dives within the entire dataset were viable for validation trials and were analyzed to measure the quality of volunteer collected data. The mean similarity index, accuracy, and correctness of abundance ratings (CAR) parameters were tested in accordance with the standard methodology for this citizen science project [20].

Temporal Analysis
Two reduced datasets containing only the best subsets of taxa from any individual year were obtained, and resemblance matrices were again created from Bray-Curtis distances of both the abundance taxa data and the overall-transformed p/a taxa data (Bray-Curtis similarity, [35]). To test significant difference among years PERMANOVAs [39] were run with the fixed factor "year". For a sharper visualization of temporal variation in community structure, distances between centroids were obtained from the resemblance matrices using "Year" as the grouping factor and visualized with 2D and 3D MDS plots. 3D plots were used to better interpret the direction of vectors in 2D plots and were not included as figures in the current manuscript. Any similar groupings of the centroidal "years" were found with a hierarchical cluster analysis (CLUSTER; [40]). The average sample taxa abundance and presence/absence from each year were calculated, and a BVSTEP Best analysis was ran from the Bray-Curtis resemblance matrix to find the subset of taxa that best explained the community structure variance within centroid data sets (BVSTEP, [37]).

Data collection and isolation
A total of 390 questionnaires were collected from 2007 to 2014 (Table 1). Questionnaire counts per year ranged from 12 to 84, and significantly decreased over time (R 2 = 0.78, P < 0.05). Over the 8 years, 71 (97%) of the 72 target taxa present on the STE questionnaire were sighted (all taxa excluding the Manta Ray).

Preliminary treatment and analysis
According  Table 1).
In all other years there was no significant relationship ( Table 2; Appendix Table 1 Table 2; Appendix Table 2). In 2008 all the diving parameters except FD were significantly correlated to the taxa abundance (P < 0.05; Table 2; Appendix Table   2). PY, AD, and FD were significantly related to taxa abundance in 2009 (P < 0.05; Table 2; Appendix Table 2). In 2012 only PY and T dive parameters were significantly related to the community structure (P < 0.05; Table 2; Appendix Table 2).  Table 3). In 2008 PY, MD, T, and RTD were significant (P < 0.05; Table 2, Appendix Table 3), while only PY, T and RTD resulted significant in 2012 (P < 0.05).
Of the 7 validation trial dives, the average mean similarity index score was 98.8%, the average mean accuracy score was 57.1% and ranged from 40.4 to 70.0%, and the mean average CAR of 73.8%.

Temporal Analysis
According to the PERMANOVA from both the taxa abundance and p/a data, the community structure  (Figs. 2a and 3a). Given the high stress value (> 0.3) of these MDS plots, further centroidal MDS plots of both taxa data sets were generated to have a clearer, even if more general, visualization highlighting the similar groupings of years within their respective distances resulting from the hierarchical cluster analysis (Figs. 2b and 3b).  The subset of taxa that best explained the distances between yearly centroids of community structure for abundance data were the Soft Tree Coral, Giant Moray, Red Sea Clownfish, Napoleon Wrasse, and Caranxes. The best subset of taxa explaining the distances between yearly centroids of community structure for the p/a data were the Soft Tree Coral, Giant Moray, Squirrel Fish, Humpback Batfish, and Caranxes. Tables 3 and 4 show the relative abundance and SF% of the best taxa through the 8 years.  2007  77  48  64  26  117  2008  69  26  98  27  94  2009  41  78  80  59  125  2010  98  93  44  37  154  2011  61  63  49  47  114  2012  31  121  35  55  93  2013  50  67  17  50  8  2014  154  62  31  0  115   Table 4. Yearly sighting frequencies of best species subset from centroid aggregated p/a data.

Discussion
We report here the first investigation on the temporal community trends of one of the most famous and historically significant wrecks worldwide [23] and one of the most visited dive sites in the Red Sea [22]. Scientific literature concerning long-term (more than 5 years) temporal trends and monitoring of community structure on wreck and/or artificial reefs is scarce [32]. There is extensive interest, however, in the role of artificial reefs, (including wrecks and scuttled ships) as a conservation tool, potentially serving as a compensatory habitat for anthropogenically damaged natural reefs [41,42,43], and as possible refugia assisting corals in the colonization of cooler waters in response to ocean warming [32]. Much of the work regarding artificial coral reefs examine differences between colonization patterns on various substratum material or recently scuttled ships (less than 3 years) and natural reefs, which are largely short-term studies [41,42,43,45,46]. The few studies that have investigated community structure change over the long term (more than 5 years) have primarily been snapshot comparisons between the virgin and mature communities [47,48] or follow succession patterns of virgin artificial reefs [49].
The present study begins to fill this gap through biodiversity [50]. The Kingston, a 119-year-old shipwreck 10 km east of the Thistlegorm, mimics the surrounding natural reef communities due to its similarities in structural complexity [51]. Oil jetties in Eilat (northern Red Sea) studied as artificial reef proxies, have even higher species richness in fish assemblages than neighboring natural reefs [46] due to increased structural complexity. Studies of artificial reefs and wrecks in other regions throughout the world have shown similarly promising results with respect to high community biodiversity, supporting the findings at the SS Thistlegorm.
In the Florida Keys, artificial reef fish communities mirror those of natural reefs in terms of fish numbers and species composition [52] and two thirds of genera from neighboring natural reefs can thrive on sunken shipwrecks within the Caribbean [32]. Even in temperate seas, such as the North Sea along the Belgian Continental Shelf, shipwrecks are described as biodiversity hotspots of hard substrate communities in an otherwise sandy bottom environment while protecting nearby softbottom communities from fishing activities [33,34].
The number of observations collected in this study through recreational citizen science methods decreased in time (Table 1). This is ascribable to the reduction in overall tourism due to political upheaval consuming Egypt during the revolution and the following transitional period of the "Arab Spring" [53,54]. As tourists seek to vacation in more stable and safe countries, participation in citizen science is inherently affected. This temporal non-homogeneity is an evident and acknowledged limitation of citizen science, and thus it should not outweigh the benefits (e.g., increasing participant education and awareness, reduction of economic and time costs associated with environmental monitoring, and acceptable level of accuracy in data collection) of such a monitoring approach [19,20,50,55].
Diving parameters were significantly corelated with the taxa data in some of the years. PY was the most frequently related dive parameter, appearing in all years with a significant relationship to diving parameters for both the taxa abundance and taxa p/a data sets (  Table 4). Some of the variation in community structure over time in this study could thus be due to the differing seasonal environmental conditions in which questionnaires were collected. Total macroalgal biomass and community structure on coral reefs are strongly linked to seasonality and temperature variation in the Red Sea [55], where variation in benthic communities also affect the distribution of fish species [57]. Species richness, diversity, and evenness of reef fish assemblages vary significantly between summer and winter in the Red Sea [58], and these seasonal variations may be mirrored on wrecks and artificial reefs close to natural coral reefs.
Temperature, the next most linked dive parameter (Table 2) is generally influenced by both seasonality and depth. In this study, T is more likely a function of the seasonal variation because the range in average depth (AD) and Maximum Depth (MD) throughout the years is quite small (19)(20)(21)(22) m and 25-28 m respectively) (Appendix Table 4). In fact, coral species that dominate shallows depths tend to occupy more narrow depth ranges while species that can colonize deeper in the water column have wider depth ranges (Chow et al. 2019), suggesting that the small range of average depth in surveys at the Thistlegorm, even if statistically significant, is trivial in terms of its effect on the community structure variation given the greater depth at which the surveys were conducted. This is especially true when considering non-sessile benthic and/or free-swimming taxa. The yearly average dive duration ranged from 41 to 55 minutes (Appendix Table 4) and presented a significant correlation in 2007 and 2008 for both data sets and in 2012 for the taxa p/a data ( Table 2). Dive duration certainly Table 3).
The taxa abundance data revealed more variance among years than taxa p/a data, and even if the general trend is quite similar, two of the five best species best explaining community structure variations among years are different in the analysis of the two data sets (the Red Sea Clownfish and Napoleon Wrasse for p/a data vs. the Squirrel Fish and Humpback Batfish for abundance data). This partial inconsistency may depend on the different reliabilities of volunteer collected data between presence/absence and abundance data sets. In fact, the correctness of abundance parameter ratings ranges from 41 to 82 percent in validation trials of the entire STE project (and from 64 to 82 percent in the present analysis) while groups of volunteers show better performances in correctly identifying the presence of taxa [19,20]. In 7 validation trials from the SS Thistlegorm, the parameters of data quality measurements were in line with previous analyses on coral reef environment types within the STE project [20]. The SS Thistlegorm average mean accuracy scored 57.1 percent within a range of 46 to 70 percent, coinciding with the results from [20] where 94 percent of trials scored a mean accuracy between 40 and 70 percent. From the similarity index (SI) parameter all 7 of the dives analyzed here resulted in high levels of precision (SI, 95% CI lower bound > 75% ≤ 100%), whereas only 0.4 percent of trials in [20] obtained these similarity index scores. Notwithstanding the small number of validation trials ran on the SS Thistlegorm data set with respect to the entire STE project [20], the results highly resemble those of the more rigorous trials analyzed in [20] and can therefore provide enhanced reliability to the data that has been used for evaluating the community structure variance over time at this particular dive site.
In conclusion, there was no clear sequential shift of the SS Thistlegorm community structure over the eight years of monitoring, but some fluctuation around a general cluster characterized by high biodiversity, mainly driven by yearly relative changes in the frequency of a few species. The temporal analysis may have been slightly biased by the different average PY (i.e., seasonality) of collected data, but this bias is likely to be minimal, as the years in which PY was significantly related to taxa data are mainly included in the general cluster and do not display unusual patterns. The community structure at the SS Thistlegorm showed relative stability over time, making this artificial reef a possible and promising refugia for Red Sea biodiversity. Further investigation on the influence of artificial reefs as an auxiliary tool for human-influenced decline could entail the comparison of multiple wreck sites within the northern Red Sea to nearby natural coral reef dive sites through species abundance analyses between and among groupings of sites, possibly by latitudinal and/or temperature gradients.